What Is Artificial Intelligence and How Can It Help Your Business?

All businesses face constraints that stand in the way of growth and customer success, especially when it comes to working quickly at scale. AI can help overcome many of these constraints, evening the playing field for small and medium businesses competing in the digital sphere. What’s more, AI tools are now accessible and affordable for small and medium players. But as with any emerging technology, the key to taking advantage of AI is to understand the problem you want to solve—or opportunity you want to explore—before investing. The goal isn’t to “do AI.” It’s to overcome a clearly defined barrier or enhance a specific operation by applying AI well.

Countless AI services are now available, from customer service chatbots to algorithms that help manufacturers better manage their supply chains. No matter the use, the first step in any AI implementation is to understand what the technology can—and cannot—do.

In the same way that you need to train an employee to perform their job optimally, you need to train best-practice and reasoning into your AI systems.

—Matt Crespi, co-founder, Carnegie Mellon Corporate Startup Lab

What Is Artificial Intelligence?

AI refers to the use of computers and software to replicate the problem-solving and decision-making functions of the human mind. This is an important capability in today’s world, where the ability to collect and store data has exceeded any individual’s ability to organize and make sense of it. “AI is the most promising tool businesses have to finally catch-up and fulfill the potential of all the data at their disposal,” says Matt Crespi, PhD, co-founder of the Carnegie Mellon Corporate Startup Lab.

In practice, AI helps companies to overcome the human and logistical constraints of handling complex tasks at scale. Some constraints can’t be overcome, like the 7,400 miles between Shanghai and New York that make it impossible to ship a package between them overnight. But others are simply problems of size, or “scale.” “Consider a company that wants to track market sentiment for its brand based on people’s online and social media activity,” says Crespi. “It used to be impossible for a single person or small team to analyze 4,000 tweets per day, but with assistance from AI, that’s now doable.”  

The Difference between AI and Machine Learning

The terms artificial intelligence and machine learning are often used interchangeably, though most experts consider machine learning a subfield of AI. Both process information and make decisions based on circumstances, but they have a crucial distinction for business purposes: AI systems attempt to replicate the way in which human workers take on particular tasks, whereas machine learning algorithms make sense of large-scale data and improve their decision-making over time by observing the successes and failures of their prior decisions.

For instance, the algorithms that music streaming services use to recommend songs are based on machine learning, matching your listening data to pre-defined tags applied to all the other music in their libraries to find similar songs and artists. By contrast, personal assistants like those in smart speakers are considered AI because they process natural language commands and apply reason to them to answer complex questions.

That’s not to say AI is better than machine learning, or vice versa. It simply means that each has its place depending on your specific needs.

Applications of AI That Can Help Your Business

New applications for AI emerge each day, but the following uses have already proven successful across multiple industries, helping businesses of all sizes to scale up, improve their performance, and operate more safely.

1. Customer Analytics and Personalization

There exists a treasure trove of data that could hold immense value for your business, in both structured (e.g., the rows and columns of information in your customer database) and unstructured (customer and prospect emails, social media tweets and posts, videos, etc.) form. The ability to collect, combine, and analyze this data in context paints a more complete picture of customers, positioning you to meet their needs and market to them more accurately.

Take recommendation engines, which draw on customer data to recommend products or services tailored to their needs. These have exploded in recent years as online marketplaces replace physical storefronts. An even simpler application is the creation of personalized marketing emails that are automatically customized to each recipient. While hardly new, personalized emails remain one of the most effective tools in the marketer’s arsenal.  

A word of caution from Crespi, however: “Recommendation engines also have an Achilles heel in that they show us correlations, not causes, so it’s important to approach their output with a critical mind. And of course, to get feedback from customers to make sure your content is relevant—and not creeping them out.”

2. A Real-Time Pulse on Customer Sentiment

Natural language processing (NLP) algorithms are a powerful form of AI that allows businesses to understand market sentiment across a range of sources. While computers can’t pick up on the nuances of speech to the same degree as a human being, NLP algorithms improve each day. Many are available free—at least to start—so all it takes is one employee with IT know-how to run sentiment analyses on a particular Twitter hashtag, or pull together public opinion from online forums.

3. Cybersecurity

Cybersecurity is both crucial and incredibly complex, especially with ransomware and phishing on the rise. There is simply no way for human beings to monitor so many threats manually, much less spot anomalies in the thousands or millions of data points a business might collect. That’s exactly the kind of analysis machine learning algorithms excel at—identifying patterns in enormous pools of information and matching them to past threat patterns to proactively identify issues before they snowball into a full-on attack. Few businesses have the resources to build such cyber defenses, which is why most buy powerful machine learning solutions from specialized cybersecurity vendors.

4. Supply Chain Efficiency

Many businesses that manufacture physical goods use AI algorithms to smooth out the wrinkles in their supply chains. The delicate balance between shipping costs, inventory management, stock management, and endless disruptions that hit supply chains each day can eat into profit margins and, in the worst case, lead to dissatisfied customers. AI algorithms, combined with user-friendly data dashboards, give manufacturers transparency across every step of the chain so they can better anticipate disruptions, uncover opportunities for process improvements, and focus their resources where they are needed most at any given moment.

Across every use case, the best applications of AI involve three factors: a clear goal, the right tools and expertise, and the patience to build, test, and refine your approach until the algorithms do exactly what you set out to achieve. “In the same way that you need to train an employee to perform their job optimally, you need to train best-practice and reasoning into your AI systems,” says Crespi.